Using Reddit to track sentiment of Love Island Contestants
Author
Tucker Trost
Background
Over the past few weeks, I’ve been diving headfirst into the world of Natural Language Processing (NLP), and honestly it’s been kind of mind-blowing. Learning the tools and exploring the endless use cases has completely opened my eyes to just how powerful NLP can be. So much of the world’s data is text, and being able to actually make sense of it and pull meaningful insights? Total game changer.
One area I’ve been particularly obsessed with is sentiment analysis, figuring out whether a piece of text sounds positive, neutral, or negative. I started off with the basics, experimenting with built-in datasets from the NLTK library and playing around with the VADER lexicon. But the real magic started happening when I got my hands on pretrained models from Hugging Face, models that are trained on specific types of text like tweets.
Now here’s where things took a turn. My wife recently got into Love Island USA, a show where a bunch of attractive 20-somethings date each other in a villa in Fiji. I originally watched to be a supportive husband… but I’ll admit it, I got hooked. The drama, the storylines, the unexpected twists, I was all in. And somewhere along the way, after watching my opinion of a contestant totally shift over time, it hit me: what if I could track public sentiment like that?
That spark turned into this project—analyzing Reddit discussions of Love Island USA to track how the internet feels about each contestant as the season unfolds.
Grabbing Data
Grabbing data for this was surprisingly simple. There is a strong community following for the show under the subreddit r/loveislandusa. Within this subreddit, there are post-episode discussion threads for each episode. Each episode tends to get around 8k-15k comments, which is plenty for me to run my analysis.
Reddit also makes it very easy to use their data. After signing up for an api key, a simple call to grab all the relevant comments from each episodes discussion thread got me all the data I needed to get going.
Show the code
import pandas as pdli_full = pd.read_csv('li_full.csv')li_full[['comment','score','created_utc','episode_title']].head()
comment
score
created_utc
episode_title
0
you vote for ace and iris to couple up and he’...
746
1.749867e+09
Season 7 - Episode 10 - Post Episode Discussion
1
you vote for ace and iris to couple up and he’...
746
1.749867e+09
Season 7 - Episode 10 - Post Episode Discussion
2
you vote for ace and iris to couple up and he’...
746
1.749867e+09
Season 7 - Episode 10 - Post Episode Discussion
3
Huda is so insanely childish, she 100% gave Je...
674
1.749867e+09
Season 7 - Episode 10 - Post Episode Discussion
4
Huda is so insanely childish, she 100% gave Je...
674
1.749867e+09
Season 7 - Episode 10 - Post Episode Discussion
Sentiment Analysis
Now that I had my data, it was time to do some named-entity recognition, to pull the information I neeed. For example, if a comment talkes negatively about the contestant Huda but positivly about Jeramiah, I need to make sure I get the proper sentiment scores for each contestant within that single comment.
I was also able to use a pretrained model from hugging face that is trained specifically on the casual nature of discussion boards like twitter and reddit. This will work perfectly for our analysis
Show the code
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Downloading nvidia_cublas_cu12-12.6.4.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (393.1 MB)
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Downloading nvidia_cuda_cupti_cu12-12.6.80-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (8.9 MB)
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Downloading nvidia_cuda_nvrtc_cu12-12.6.77-py3-none-manylinux2014_x86_64.whl (23.7 MB)
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Downloading nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (897 kB)
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Downloading nvidia_cudnn_cu12-9.5.1.17-py3-none-manylinux_2_28_x86_64.whl (571.0 MB)
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Downloading nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (200.2 MB)
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Building wheels for collected packages: swifter
Building wheel for swifter (pyproject.toml) ... -
\ done
Created wheel for swifter: filename=swifter-1.4.0-py3-none-any.whl size=16589 sha256=46d2fc18a0cca687fde063e50bb0dd17dcc985aa313f202704a43c03b3d9404f
Stored in directory: /home/runner/.cache/pip/wheels/ef/7f/bd/9bed48f078f3ee1fa75e0b29b6e0335ce1cb03a38d3443b3a3
Successfully built swifter
Installing collected packages: nvidia-cusparselt-cu12, mpmath, zipp, triton, tqdm, toolz, sympy, safetensors, regex, pyarrow, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufile-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, networkx, locket, hf-xet, fsspec, filelock, cloudpickle, click, partd, nvidia-cusparse-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, nltk, importlib_metadata, huggingface-hub, tokenizers, nvidia-cusolver-cu12, dask, transformers, torch, swifter
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Successfully installed click-8.2.1 cloudpickle-3.1.1 dask-2025.5.1 filelock-3.18.0 fsspec-2025.5.1 hf-xet-1.1.4 huggingface-hub-0.33.0 importlib_metadata-8.7.0 locket-1.0.0 mpmath-1.3.0 networkx-3.5 nltk-3.9.1 nvidia-cublas-cu12-12.6.4.1 nvidia-cuda-cupti-cu12-12.6.80 nvidia-cuda-nvrtc-cu12-12.6.77 nvidia-cuda-runtime-cu12-12.6.77 nvidia-cudnn-cu12-9.5.1.17 nvidia-cufft-cu12-11.3.0.4 nvidia-cufile-cu12-1.11.1.6 nvidia-curand-cu12-10.3.7.77 nvidia-cusolver-cu12-11.7.1.2 nvidia-cusparse-cu12-12.5.4.2 nvidia-cusparselt-cu12-0.6.3 nvidia-nccl-cu12-2.26.2 nvidia-nvjitlink-cu12-12.6.85 nvidia-nvtx-cu12-12.6.77 partd-1.4.2 pyarrow-20.0.0 regex-2024.11.6 safetensors-0.5.3 swifter-1.4.0 sympy-1.14.0 tokenizers-0.21.1 toolz-1.0.0 torch-2.7.1 tqdm-4.67.1 transformers-4.52.4 triton-3.3.1 zipp-3.23.0
import nltkfrom nltk.tokenize import sent_tokenizenltk.download('punkt')# Create Sentiment Function# def targeted_sentiment(comment, islanders):# islander_sentiment = {}# for sentence in sent_tokenize(comment):# for name in islanders:# if re.search(rf'\b{name}\b', sentence, re.IGNORECASE):# result = get_sentiment_score(sentence)# if name not in islander_sentiment:# islander_sentiment[name] = []# islander_sentiment[name].append(result['compound'])# return {name: np.mean(scores) for name, scores in islander_sentiment.items()}# Apply to data# loveisland['islander_sentiment'] = loveisland['comment'].swifter.apply(lambda x: targeted_sentiment(x,islanders))# loveisland.head()
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# import ast# # Step 1: Convert stringified dicts to real dicts# loveisland2['islander_sentiment'] = loveisland2['islander_sentiment'].apply(# lambda x: ast.literal_eval(x) if isinstance(x, str) else x# )# # Step 2: Filter out nulls and make sure only dicts are kept# loveisland2_clean = loveisland2[# loveisland2['islander_sentiment'].apply(lambda x: isinstance(x, dict) and len(x) > 0)# ].copy()# # Step 3: Convert dict into list of tuples and explode# loveisland2_clean['islander_sentiment_items'] = loveisland2_clean['islander_sentiment'].apply(lambda d: list(d.items()))# loveisland2_exploded = loveisland2_clean.explode('islander_sentiment_items')# # Step 4: Split tuple into two new columns# loveisland2_exploded[['islander', 'sentiment']] = pd.DataFrame(# loveisland2_exploded['islander_sentiment_items'].tolist(), index=loveisland2_exploded.index# )# # Optional cleanup# loveisland2_exploded = loveisland2_exploded.drop(columns=['islander_sentiment_items'])# loveisland2_exploded.head()
After running the model on our comments and pulling in some episode airdate data, we wind up with the following dataset.